Predicting resistance to disease

ABSTRACT

The Invention relates to a method of predicting resistance to infectious pancreatic necrosis in salmon, the method comprising determining the alleles present at a DNA polymorphism in the salmon and predicting whether or not the salmon is resistant to infectious pancreatic necrosis based on the determination of the alleles. The invention also relates to a method of selecting a salmon for use as broodstock, wherein the salmon is selected based on the prediction by the first method that the salmon will have resistance to infectious pancreatic necrosis.

The present invention relates to methods for predicting resistance toinfectious pancreatic necrosis in salmon, more specifically theinvention relates to predicting such resistance by the analysis of DNApolymorphisms.

Infectious pancreatic necrosis (IPN) is one of the major threats to thesalmon farming industry worldwide. The disease is caused by an aquaticbirnavirus, causing necrosis of pancreatic cells and liver cells,resulting in lethargy and sudden mortality. The virus is wide-spread innature, but does not seem to affect free-living salmon to any largeextent. In aquaculture environments, the disease causes mortalities bothat the fry stage, when the fish are still living in fresh water, and atthe post-smolt stage, shortly after transfer to sea water. Theindustry-wide losses due to IPN have been estimated to be 8% during thefresh water phase and 5% during the sea phase.

The salmon industry is, generally speaking, divided into several stratacorresponding to the different life stages of the fish: egg producerssell fertilised eggs to producers of smolt, who provide salt-water-readyfish (smolt) to grow-out-producers. For each strata it is advantageousto select eggs or fish that are above-average resistant to diseases.Salmon breeding companies run continuous fish selection programmes aimedat improving the aquaculture stocks with regards to disease resistance,and protocols have been developed for testing of fish's resistance toseveral specific diseases. These challenge tests have been used in orderto select fish as broodstock that possess above-average resistance tothe diseases in question. Conventional tests involve controlledchallenge-testing of siblings of the breeding candidates. Thismethodology is, however, impeded by the fact that infected fish cannotbe used as broodstock. One therefore has to resort to selecting random(un-tested) animals from the families of the tested fish that performedbest in the challenge tests (so-called family selection).

There is therefore a need for alternative methodologies for assayinganimals' resistance to infectious pancreatic necrosis; particularlymethodologies that allow direct assaying of individual's resistance toinfectious pancreatic necrosis, whilst retaining the possibility ofusing the tested animal as broodstock.

The inventor of the present invention has, following extensiveexperimentation, identified that one can predict resistance toinfectious pancreatic necrosis in salmon by analysis of one or more DNApolymorphisms (thereby satisfying the aforementioned need).

Accordingly, in a first aspect of the present invention, there isprovided a method of predicting resistance to infectious pancreaticnecrosis in salmon, the method comprising determining the allelespresent at a DNA polymorphism in the salmon and predicting whether ornot the salmon is resistant to infectious pancreatic necrosis based onthe determination of the alleles.

The inventor has found that the DNA polymorphisms of the presentinvention can be present in either of two forms, i.e. the polymorphismshave two alleles. One allele can be characterised as being predictive ofresistance to infectious pancreatic necrosis (i.e. the resistanceallele); the other being predictive of non-resistance to infectiouspancreatic necrosis (i.e. non-resistance allele). Salmon are diploidorganisms, and so possess two copies of the polymorphisms of the presentinvention (one copy to be found in each set of chromosomes). The step ofdetermining the alleles in the method of the first aspect of the presentinvention therefore includes the step of analysing the DNA polymorphismprovided in each set of chromosomes in order to determine whether eachcopy of the DNA polymorphism present is a resistance allele or is anon-resistance allele. When a salmon subjected to the method of thepresent invention is determined to have two copies of the resistanceallele for the DNA polymorphism (i.e. the salmon is homozygous for theresistance allele), the salmon is predicted to have resistance toinfectious pancreatic necrosis. Conversely, when a salmon subjected tothe method of the present invention is determined to have two copies ofthe non-resistance allele for the DNA polymorphism (i.e. is homozygousfor the non-resistance allele), the salmon is predicted not to haveresistance to infectious pancreatic necrosis. It may be concluded that asalmon that is predicted by the method of the present invention ashaving infectious pancreatic necrosis resistance has a greater thannormal chance of having infectious pancreatic necrosis resistance.Conversely, it may be concluded that a salmon that is predicted not tohave infectious pancreatic necrosis resistance has a lower than normalrisk of developing infectious pancreatic necrosis resistance. When asalmon subjected to the method of the present invention is determined tohave one copy of the resistance allele for the DNA polymorphism and onecopy of the non-resistance allele for the DNA polymorphism (i.e. isheterozygous), the salmon would not be predicted according to thepresent invention to have resistance to infectious pancreatic necrosis.However, that salmon would be predicted to have a greater chance ofbeing resistant to infectious pancreatic necrosis than a salmon with twocopies of the non-resistance allele. Henceforth, such as salmon will bereferred to as having semi-resistance to infectious pancreatic necrosis.

The DNA polymorphism in question can be any of several DNA polymorphismsfound by the inventor to have this predictive ability. All of these DNApolymorphisms are located on chromosome 26. The DNA polymorphisms arelinked by their common feature of predicting resistance to IPNresistance. The ability of the DNA polymorphisms to predict resistanceto IPN can be quantified using the r² statistic, which will be explainedbelow. All the DNA polymorphisms share the characteristic that this r²statistic is larger than 0.3. The DNA polymorphism may be a multiplenucleotide polymorphisms (ie non-SNP polymorphisms) a single nucleotidepolymorphism, an addition mutation, or a deletion mutation. Each type ofDNA polymorphism provided above are contemplated individually as part ofthe present invention for the step of determining in the methods of thepresent invention.

The DNA polymorphism may be selected from any of the DNA polymorphismsprovided in Table 1. Each of the DNA polymorphisms provided in Table 1are contemplated individually as part of the present invention.

The DNA polymorphisms described throughout this application are definedwith reference to the whole genome sequence for Salmo salar published ingenebank under accession number AGKD00000000 (version AGKD00000000.1 GI:354459050). More particularly, each DNA polymorphism in the presentapplication derives its name as described herein from the following:Genbank accession number, followed by underscore (‘_’) followed by theposition of the DNA polymorphism within the GenBank sequence, followedby square brackets enclosing the reference allele (appearing first) andthe alternative allele (appearing second).

The reference allele is the allele appearing in the reference sequence.

For example, the DNA polymorphism may be:

-   -   AGKD01281000.1_4157 [T/TA];    -   AGKD01281000.1_5527 [T/TAT];    -   AGKD01021775.1_19790[G/A];    -   AGKD01281000.1_5251 [A/G],    -   or;    -   AGKD1281000.1_4338 [A/T].

Each of the above DNA polymorphisms are contemplated individually aspart of the present invention.

The method may employ two DNA polymorphisms. When the method is employedwith two DNA polymorphisms, the two DNA polymorphisms constitute oneunit, hereafter referred to as a haplotype. Each haplotype can have fourdifferent alleles, corresponding to the four different combinations ofDNA polymorphism alleles at the individual DNA polymorphisms (forexample, if the haplotype is made up of one DNA polymorphism withalleles A and T, and one DNA polymorphisms with alleles T and G, thefour possible haplotype alleles are A-T, A-G, T-T, and T-G). Each ofthese four alleles would be either a resistance allele or anon-resistance allele, in a manner analogous to the single DNApolymorphism method laid out above. Thus, in the hypothetical case of ahaplotype having the four alleles A-T, A-G, T-T, and T-G, it could bethat all A-T, A-G, and T-T were resistance alleles, whereas T-G was anon-resistance allele. In that case, an animal having one copy of theA-T allele and one copy of the A-G allele would be resistant to IPN, ananimal having one copy of A-T and one copy of T-G would besemi-resistant, while an animal having two copies of T-G would benon-resistant.

The inventor has discovered a large number of such haplotypes, ie.combinations of two DNA polymorphisms, that are powerful predictors ofresistance to IPN, more powerful than single DNA polymorphisms. For eachof these haplotypes, the inventor has identified which alleles areresistance alleles and which alleles are non-resistance alleles. Thepairs of DNA polymorphisms that make up predictive haplotypes are eitherany combination of DNA polymorphisms listed in Table 1, or they are anycombinations of one DNA polymorphism from Table 1 with one DNApolymorphism from Table 2. All predictive haplotypes are listed in Table3, where the DNA polymorphisms are denoted by numbers relative to Tables1 and Table 2. Each of the pairs of DNA polymorphisms are contemplatedfor use individually as part of the present invention. All pairs of DNApolymorphisms share the characteristic that their r² value (to bedescribed below) is larger than 0.6.

Consequently, the present invention may therefore relate to a methodthat further comprises the step of determination of the allele presentat a further DNA polymorphism, and a prediction of whether or not thesalmon is resistant to infectious pancreatic necrosis is based on thedetermination of the alleles at both DNA polymorphisms.

For example, the method of the present invention may include thedetermination of alleles present at the DNA polymorphismAGKD01458345.1_5634[G/T], and at the further DNA polymorphismAGKD01021775.1_19790[G/A], and a prediction of whether or not the salmonis resistant to infectious pancreatic necrosis is based on thedetermination of the alleles at both DNA polymorphisms.

When haplotypes of two DNA polymorphisms rather than single DNApolymorphisms are used for predicting resistance, the haplotype allelesmust first be determined in the tested fish, in other words, it must bedetermined which alleles at the individual DNA polymorphism are locatedon the same chromosomes. This can be done using computer programs suchas PHASE (website stephenslab.uchicago.edu/software.html#phase),although for most animals the haplotype alleles will be evident (e.g. ifan animal has two copies of allele A at one DNA polymorphism, and onecopy of T and one copy of G at the other DNA polymorphism, only twoconfigurations of alleles at the haplotype is possible, namely A-G+A-T).

When a haplotype of two DNA polymorphisms are used rather than one DNApolymorphism, the test becomes more predictive compared to when only oneDNA polymorphism is used.

The method may involve analysis of more than two DNA polymorphisms. Forexample, the method of the present invention may involve thedetermination of more than two polymorphisms, wherein at least one ofthe polymorphisms is provided in table 1 and/or at least two of thepolymorphisms are provided as a pair in table 3.

The method may be applied to Atlantic salmon (i.e. Salmo salar).

The step of determining the presence or absence in a salmon may bepractised on a sample taken from the salmon. The sample may be anysample in which analysis of nucleic acid material is possible, as wouldbe readily understood by the person skilled in the art. For theavoidance of doubt, the sample may be a muscle tissue sample, bloodsample, liver sample and/or a fin dip.

The skilled person would be well aware of all available methods capableof testing for the presence or absence of a DNA polymorphism. Forexample, the method may involve sequence analysis of the salmon to betested. Alternatively, the method may involve single base extension ofDNA fragments terminating at the polymorphic site (e.g. IPLEX assaysfrom Sequenom and Infinium assays from Illumina), allele-specific PCR(e.g. SNPtype assays from Fluidigm or KASPar assays from KBiosciences),or competitive hybridisation of probes complementary to the differentalleles (e.g. the TaqMan assay from Applied Biosystems).

Consequently, in a further aspect of the present invention, there isprovided a hybridisation probe that is specific for one or more of theaforementioned DNA polymorphisms.

The DNA at and around the DNA polymorphisms can be extrapolated from thenames given to the DNA polymorphisms in Table 1 and Table 2. Inaddition, the DNA sequences at and around the DNA polymorphisms can befound in Table 4. Hybridisation probes that are selective for these DNAsequences may form part of the present invention.

A salmon that is predicted to have resistance to infectious pancreaticnecrosis according to the first aspect of the present invention is morelikely than normal to produce offspring that have a higher than normalchance of having resistance to infectious pancreatic necrosis.Consequently, in a further aspect of the present inventions, there isprovided a method of selecting a salmon for use as broodstock, whereinthe salmon is selected, based on the prediction by the method as claimedin the first aspect of the present invention, to have resistance toinfectious pancreatic necrosis.

Conversely, a salmon predicted by the method of the first aspect of thepresent invention as not having resistance to infectious pancreaticnecrosis would not be selected as broodstock.

The present invention also relates to an isolated polynucleotidecomprising one or more of the single DNA polymorphisms selected from thegroup provided in Table 1 located within a portion of the salmon genome.Exemplary sequences for such isolated polynucleotides may be found inTable 4.

The terms “haplotype allele” and “DNA polymorphism allele” take theirnormal meaning as would be well understood by the person skilled in theart. However, for the avoidance of doubt “DNA polymorphism allele” maymean one of two different nucleotide sequences at the site of a DNApolymorphism of the present invention (one allele being the “resistanceallele”, the other being the “non-resistance allele”). However, for theavoidance of doubt, “haplotype allele” may mean one of four possiblepairs of DNA polymorphism alleles of the present invention. or:

. . . “haplotype allele” may mean any possible unique combination ofalleles for that haplotype, i.e. any unique combination of one allelefrom each of the DNA polymorphisms constituting the haplotype (in thecontext of haplotypes constituted by two bi-allelic DNA polymorphisms,four such combinations are possible) The present invention will now bedescribed by way of example with reference to the accompanying figures,in which:—

FIG. 1 shows a graph illustrating survival rates of salmon in aninfectious pancreatic necrosis—challenge test.

FIG. 2 shows a graph illustrating cumulative mortality in a bathchallenge of salmon with standard virus isolate C-1244.

FIG. 3 shows a graph illustrating cumulative mortality in a bathchallenge of salmon with a Norwegian field strain of IPN virus.

1. SELECTION OF TEST ANIMALS

Forty-five Atlantic salmon from the Aqua Gen breeding nucleus in Norway(selected from among the parents of the 2005 and 2008 year classes) werechosen for massive parallel sequencing (Illumina Hi Seq 2000). Allsalmon in the breading nucleus are derived from salmon taken fromNorwegian rivers.

A Quantitative Trait Loci (QTL) has been linked with IPN resistance inAtlantic Salmon (Moen et al. 2009). Three single DNA polymorphisms wererecently reported as being associated with the QTL (Houston et al.2012), but a test for deducing whether individual animals are resistantor non-resistant has not been presented. The QTL is located onchromosome 26.

We assume here that the above-mentioned QTL for resistance to IPN iscaused by an underlying but unknown mutation within a gene or otherfunctional DNA element. This unknown mutation will hereafter be referredto as the quantitative trait nucleotide (QTN). It is further assumedthat the QTN has two alleles; one allele that gives increased resistance(resistance allele, Q) and one allele that gives decreased resistance(non-resistance allele, q).

Four hundred and fifty-four full-sib groups of Atlantic salmon fry werechallenged in individual tanks shortly after the start of feeding(protocols for a standard challenge test can be found in Moen et al.2009. Each full-sib group consisted of 103 fish (on average), and tissuesamples were collected from the 10 first-to-die within group as well as10 survivors (or 10 last-to-die), whereupon DNA was extracted using theDNAeasy kit from QIAGEN (QIAGEN, Venlo, the Netherlands). From 206selected full-sib groups, affected and surviving offspring weregenotyped with three microsatellite markers located within the region ofthe QTL for IPN resistance; Alu333, Ssa0384BSFU/ii and Ssa0285BSFU,whereupon the linkage phase between alleles of the three microsatelliteswere identified in each mapping parent using the observed co-segregationof alleles from parents to offspring (genotyping of microsatellitemarkers are discussed in more detail in Moen et al. 2009). Thisgenotyping was done in an iterative fashion so that, ultimately, almostall full-sib groups that were likely to have at least oneQTN-heterozygous parent (see below) were genotyped. A chi-square testwas applied in order to test for co-inheritance of thethree-microsatellite haplotype and the affected/resistant phenotype,thereby identifying 110 QTN-heterozygous parents. Using data from theseQTN-heterozygous parents, a table was created linking alleles at thethree-microsatellite haplotype to QTN alleles. (If athree-microsatellite allele was found to be linked to both Q and q, onlythe most prevalent linkage phase was entered into the table.) This tablewas next used to extrapolate genotypes at the QTN for the mappingparents found to be QTN homozygous, as well as for other animals fromthe Aqua Gen breeding nucleus. Twenty-two Aqua Gen animals deduced inthis way to have the QTN genotype QQ (i.e. expected to provide good IPNresistance), as well as 23 Aqua Gen animals likewise found to have theqq genotype (i.e. expected to provide poor IPN resistance), were chosenfor subsequent whole-genome sequencing. These sets of 22 and 23 animalswere put together in such a way as to minimise the relatedness ofanimals within the group, by maximising the diversity ofthree-microsatellite alleles within each group.

2. MAKING A REFERENCE DNA SEQUENCE ASSEMBLY FOR THE QTL REGION

QTL region was defined as the region in between the SNPs ESTNV_31602_808and GCR_cBin30387_Ctg1_91 on the Atlantic salmon SNP linkage map (Lienet al. 2011).

Bacterial Artificial Chromosome (BAC) dones matching these SNPs wereisolated from an existing BAC library (Thorsen et al. 2004). On thebasis of a physical map made from this library (www.asalbase.org), aminimum tiling path of 31 BACs was made (Table 5). Atlantic salmongenomic (i.e. Insert) DNA was extracted from each BAC. An individuallytagged paired-end library (with average insert size 350 bp) was made foreach BAC DNA sample, whereupon the samples were sequenced in multiplexon a HiSeq2000 (Illumina Inc., San Diego, USA) to an average depth ofapproximately 800 times haploid genome coverage. Following removal ofresidual adapter sequences, discarding of too-short reads, trimming ofthe ends of poor quality reads, and matching of paired-end reads, a denovo assembly was made within each BAC using the ‘clc_novo_assemble’program from the CLC Assemble Cell suite (CLC Bio, Aarhus, Denmark).Phrap version 1.090518 (http://phrap.org) was then used to assembleindividual BAC contig sequences into a set of contigs spanning all BACs.Finally, the contigs from this reference were combined into onecontiguous genomic scaffold by aligning it with scaffolds from apreliminary version of the Atlantic salmon genome sequence (which hadbeen made in-house, using the Celera Assembler software, based on thedata from the first 27 batches of sequences submitted by the sequencingproject into the NCBI Trace Archive).

TABLE 5 Bacterial Artificial Chromosome (BAC) constituting a minimumtiling path found to span the QTL region. S0042J22 S0004K18 S0161O04S0243D12 S0076E15 S0021H01 S0162F10 S0258L08 S0119L01 S0026N22 S0162J03S0259M06 S0120O19 S0048P16 S0170B06 S0262M03 S0126K07 S0063G22 S0201A04S0282P22 S0457C13 S0066E05 S0215J07 S0344A15 S0001F22 S0115B04 S0227H08S0449E20 S0001N03 S0160J02 S0236E20

3. DISCOVERY OF DNA POLYMORPHISMS PREDICTIVE OF IPN

The above-mentioned 23 QQ animals and 22 qq-animals were sequenced usingHiSeq2000 technology from Illumina. Individually tagged paired-endlibraries were made from each sample, before samples were pooled forsequencing A total of 264×10⁹ reads was produced, corresponding to aper-animal coverage of two times the haploid genome. The reads wereassembled onto the above-mentioned QTL-region reference sequence usingthe programs ‘clc_ref_assemble_long’ and ‘clc_ref_assemble’ from the CLCAssembly Cell suite, producing two assemblies corresponding to the twoQTN genotype groups. A matching length fraction of 0.9 and a minimumsimilarity of 0.98 was stipulated in an attempt to minimise the mappingof reads from homologous chromosomes. SNP detection was performed onthese separate assemblies using the program ‘find variations’ from theCLC Assembly Cell suite, allowing a minimum of one nucleotide differenceto the reference base. A Fisher's exact test was used in order to testfor independence between QTN genotype (i.e. assembly) and SNP/indelalleles. The SNPs with the most significant statistics from this exactwere genotyped in the 110 QTN-heterozygous animals mentioned above, aswell as in the challenge-tested offspring of those animals, and aFisher's exact test was performed in order to test for Independentinheritance of SNP alleles and QTN alleles. The correlation coefficient(r²) between alleles at the SNP and at the QTN, a measure of the degreeof linkage disequilibirum (LD) between loci, was also calculated foreach SNP, using the ‘LD’ function of the ‘genetics’ module of the Rstatistical program suite. A SNP was defined as useful for predictingresistance to IPN If it had an r² value above 0.3 (this is a commonassumption among geneticists, see e.g. Shifman et al, Human MolecularGenetics 2003). In the present context, the r² value is the fraction ofallelic variation of the QTL explained by the predictive DNApolymorphism. For example, if r²=0.5, twice as many animals must begenotyped for the predictive DNA polymorphism relative to a hypotheticalcase where the predictive DNA polymorphisms is the QTN Itself.

SNPs identified as most strongly correlating with IPN resistance areprovided in Table 1.

TABLE 1 DNA polymorphisms strongly associated with resistance to IPN.resistance allele/non- DNA resistance polymorphism # DNA polymorphismname allele r² 1 AGKD01281000.1_4157[T/TA] T/TA 0.57 2AGKD01281000.1_5527[T/TAT] T/TAT 0.57 3 AGKD01021775.1_19790[G/A] G/A0.57 4 AGKD01281000.1_5251[A/G] A/G 0.54 5 AGKD01281000.1_4338[A/T] A/T0.54 6 AGKD01317469.1_245[T/A] T/A 0.54 7 AGKD01281000.1_5457[A/G] A/G0.54 8 AGKD01028155.1_12812[A/G] A/G 0.5 9 AGKD01452978.1_5956[A/G] A/G0.41 10 AGKD01039267.1_12921[T/A] T/A 0.41 11 AGKD01059002.1_4664[T/C]T/C 0.4 12 AGKD01451885.1_830[T/G] T/G 0.4 13 AGKD01003456.1_35321[A/G]A/G 0.37 14 AGKD01059002.1_16264[G/A] G/A 0.36 15AGKD01452978.1_6935[A/G] A/G 0.35 16 AGKD01003456.1_36664[G/T] G/T 0.3517 AGKD01340746.1_282[C/T] C/T 0.35 18 AGKD01062103.1_13615[T/G] T/G0.32 19 AGKD01062103.1_13695[T/C] T/C 0.32 20 AGKD01007787.1_13666[G/A]G/A 0.31 21 AGKD01059002.1_3603[T/G] T/G 0.31 r² = the fraction ofallelic variation at the QTN explained by the DNA polymorphism.

4. DISCOVERY OF TWO-DNA-POLYMORPHISM HAPLOTYPES PREDICTIVE OF RESISTANCETO IPN

The genotypes of DNA polymorphisms on QTN-heterozygous parents and theirchallenge-tested offspring, described above, was also used in order tofind combinations of 2 DNA polymorphisms that were more predictive ofIPN resistance than the most predictive single DNA polymorphisms. TheDNA polymorphisms were combined in all possible two-way combinations,and for each haplotype consisting of two DNA polymorphisms, haplotypealleles were identified for each QTN-heterozygous parent, and a Fisherexact test was used in order to test for independence between haplotypeallele and QTN alleles. The correlation coefficient (r²) between allelicstates at the two-SNP haplotype and at the QTN was calculated by firstmapping the two-SNP haplotype down to a two-allele system by replacingeach allele name with the name of the QTN allele that the two-SNPhaplotype allele in question was predominantly linked to (see Table 3),followed by calculation of r² using the ‘LD’ function of the ‘genetics’module of the R statistical program suite.

The haplotypes predictive of resistance to IPN, identified in thismanner, were either combinations of two DNA polymorphisms from Table 1,or they were combinations of one DNA polymorphism from Table 1 and oneDNA polymorphism from Table 2. Table 3 contains all the combinations ofDNA polymorphisms found to have an r² value larger than 0.60. Table 3also contains the identity of the haplotypes alleles found for therespective predictive haplotypes, as well as the classification(resistant vs. non-resistant) of these haplotypes alleles.

Table 4 contains the DNA sequences of the DNA polymorphisms. Thesesequences can also be deduced on the basis of the DNA polymorphismnames, as noted above.

TABLE 2 Auxiliary DNA polymorphisms, forming diagnostic pairs of DNApolymorphisms in combination with DNA polymorphisms from Table 1. DNAresistance allele/ polymorphism # DNA polymorphism name non-resistanceallele 22 AGKD01000927.1_15806[C/G] C/G 23 AGKD01458345.1_5634[G/T] T/G24 AGKD01083029.1_8368[A/C] C/A 25 AGKD01062103.1_13615[T/G] T/G 26AGKD01062103.1_13695[T/C] T/C 27 AGKD01032349.1_7232[A/C] A/C 28AGKD01032349.1_14078[A/G] G/A 29 AGKD01051656.1_1495[T/A] A/T 30AGKD01083029.1_5084[G/C] C/G 31 AGKD01455926.1_1814[G/A] A/G 32AGKD01003456.1_1873[G/C] G/C 33 AGKD01037589.1_572[C/T] C/T 34AGKD01037589.1_1369[C/A] C/A 35 AGKD01205804.1_11559[A/G] A/G 36AGKD01106761.1_1717[T/C] T/C

TABLE 3 Predictive combinations of two DNA polymorphisms (haplotypes).r² = the fraction of allelic variation at the QTN explained by thehaplotype; haplotype alleles = the valid alleles of the haplotype,T-G(R) = a haplotype having allele T at the DNA polymorphism #1, G atDNA polymorphism #2, being a resistance allele, TA-G(N) = a haplotypehaving allele TA at the DNA polymorphism #1, G at DNA polymorphism #2,being a non-resistance allele, etc. The numbering of the DNApolymorphisms is relative to Tables 1 and 2. DNA poly- DNA poly-morphism morphism #1 #2 r² haplotype alleles 1 23 0.84 T-T(R), T-G(R),TA-T(R), TA-G(N) 1 28 0.84 T-G(R), T-A(R), TA-G(R), TA-A(N) 2 23 0.84T-T(R), T-G(R), TAT-T(R), TAT-G(N) 2 28 0.84 T-G(R), T-A(R), TAT-G(R),TAT-A(N) 3 23 0.84 G-T(R), G-G(R), A-T(R), A-G(N) 3 28 0.84 G-G(R),G-A(R), A-G(R), A-A(N) 4 23 0.81 A-T(R), A-G(R), G-T(R), G-G(N) 4 280.81 A-G(R), A-A(R), G-G(R), G-A(N) 5 23 0.81 A-T(R), A-G(R), T-T(R),T-G(N) 5 28 0.81 A-G(R), A-A(R), T-G(R), T-A(N) 6 23 0.81 T-T(R),T-G(R), A-T(R), A-G(N) 6 28 0.81 T-G(R), T-A(R), A-G(R), A-A(N) 7 230.81 A-T(R), A-G(R), G-T(R), G-G(N) 7 28 0.81 A-G(R), A-A(R), G-G(R),G-A(N) 1 11 0.79 T-T(R), T-C(R), TA-T(R), TA-C(N) 1 12 0.79 T-T(R),T-G(R), TA-T(R), TA-G(N) 2 11 0.79 T-T(R), T-C(R), TAT-T(R), TAT-C(N) 212 0.79 T-T(R), T-G(R), TAT-T(R), TAT-G(N) 3 11 0.79 G-T(R), G-C(R),A-T(R), A-C(N) 3 12 0.79 G-T(R), G-G(R), A-T(R), A-G(N) 4 11 0.78A-T(R), A-C(R), G-T(R), G-C(N) 4 12 0.78 A-T(R), A-G(R), G-T(R), G-G(N)5 11 0.78 A-T(R), A-C(R), T-T(R), T-C(N) 5 12 0.78 A-T(R), A-G(R),T-T(R), T-G(N) 6 11 0.78 T-T(R), T-C(R), A-T(R), A-C(N) 6 12 0.78T-T(R), T-G(R), A-T(R), A-G(N) 7 11 0.78 A-T(R), A-C(R), G-T(R), G-C(N)7 12 0.78 A-T(R), A-G(R), G-T(R), G-G(N) 1 31 0.76 T-A(R), T-G(R),TA-A(R), TA-G(N) 2 31 0.76 T-A(R), T-G(R), TAT-A(R), TAT-G(N) 3 31 0.76G-A(R), G-G(R), A-A(R), A-G(N) 1 10 0.75 T-T(R), T-A(R), TA-T(R),TA-A(N) 2 10 0.75 T-T(R), T-A(R), TAT-T(R), TAT-A(N) 3 10 0.75 G-T(R),G-A(R), A-T(R), A-A(N) 4 31 0.74 A-A(R), A-G(R), G-A(R), G-G(N) 5 310.74 A-A(R), A-G(R), T-A(R), T-G(N) 6 31 0.74 T-A(R), T-G(R), A-A(R),A-G(N) 7 31 0.73 A-A(R), A-G(R), G-A(R), G-G(N) 8 14 0.73 A-G(R),A-A(R), G-G(R), G-A(N) 1 22 0.72 T-C(R), T-G(R), TA-C(R), TA-G(N) 2 220.72 T-C(R), T-G(R), TAT-C(R), TAT-G(N) 3 22 0.72 G-C(R), G-G(R),A-C(R), A-G(N) 4 10 0.72 A-T(R), A-A(R), G-T(R), G-A(N) 5 10 0.72A-T(R), A-A(R), T-T(R), T-A(N) 6 10 0.72 T-T(R), T-A(R), A-T(R), A-A(N)7 10 0.72 A-T(R), A-A(R), G-T(R), G-A(N) 11 32 0.7 T-G(R), T-C(R),C-G(R), C-C(N) 11 33 0.7 T-C(R), T-T(R), C-C(R), C-T(N) 11 35 0.7T-A(R), T-G(R), C-A(R), C-G(N) 12 24 0.7 T-C(R), T-A(R), G-C(R), G-A(N)12 30 0.7 T-C(R), T-G(R), G-C(R), G-G(N) 12 32 0.7 T-G(R), T-C(R),G-G(R), G-C(N) 12 33 0.7 T-C(R), T-T(R), G-C(R), G-T(N) 12 35 0.7T-A(R), T-G(R), G-A(R), G-G(N) 1 21 0.69 T-T(R), T-G(R), TA-T(R),TA-G(N) 2 21 0.69 T-T(R), T-G(R), TAT-T(R), TAT-G(N) 3 21 0.69 G-T(R),G-G(R), A-T(R), A-G(N) 4 22 0.69 A-C(R), A-G(R), G-C(R), G-G(N) 5 220.69 A-C(R), A-G(R), T-C(R), T-G(N) 6 22 0.69 T-C(R), T-G(R), A-C(R),A-G(N) 7 22 0.69 A-C(R), A-G(R), G-C(R), G-G(N) 1 18 0.68 T-T(R),T-G(R), TA-T(R), TA-G(N) 1 19 0.68 T-T(R), T-C(R), TA-T(R), TA-C(N) 1 250.68 T-T(R), T-G(R), TA-T(R), TA-G(N) 1 26 0.68 T-T(R), T-C(R), TA-T(R),TA-C(N) 1 27 0.68 T-A(R), T-C(R), TA-A(R), TA-C(N) 2 18 0.68 T-T(R),T-G(R), TAT-T(R), TAT-G(N) 2 19 0.68 T-T(R), T-C(R), TAT-T(R), TAT-C(N)2 25 0.68 T-T(R), T-G(R), TAT-T(R), TAT-G(N) 2 26 0.68 T-T(R), T-C(R),TAT-T(R), TAT-C(N) 2 27 0.68 T-A(R), T-C(R), TAT-A(R), TAT-C(N) 3 180.68 G-T(R), G-G(R), A-T(R), A-G(N) 3 19 0.68 G-T(R), G-C(R), A-T(R),A-C(N) 3 25 0.68 G-T(R), G-G(R), A-T(R), A-G(N) 3 26 0.68 G-T(R),G-C(R), A-T(R), A-C(N) 3 27 0.68 G-A(R), G-C(R), A-A(R), A-C(N) 4 210.68 A-T(R), A-G(R), G-T(R), G-G(N) 5 21 0.68 A-T(R), A-G(R), T-T(R),T-G(N) 6 21 0.68 T-T(R), T-G(R), A-T(R), AG(N) 7 21 0.68 A-T(R), A-G(R),G-T(R), G-G(N) 11 24 0.67 T-C(R), T-A(R), C-C(R), C-A(N) 11 30 0.67T-C(R), T-G(R), C-C(R), C-G(N) 4 18 0.65 A-T(R), A-G(R), G-T(R), G-G(N)4 19 0.65 A-T(R), A-C(R), G-T(R), G-C(N) 4 25 0.65 A-T(R), A-G(R),G-T(R), G-G(N) 4 26 0.65 A-T(R), A-C(R), G-T(R), G-C(N) 4 27 0.65A-A(R), A-C(R), G-A(R), G-C(N) 5 18 0.65 A-T(R), A-G(R), T-T(R), T-G(N)5 19 0.65 A-T(R), A-C(R), T-T(R), T-C(N) 5 25 0.65 A-T(R), A-G(R),T-T(R), T-G(N) 5 26 0.65 A-T(R), A-C(R), T-T(R), T-C(N) 5 27 0.65A-A(R), A-C(R), T-A(R), T-C(N) 6 18 0.65 T-T(R), T-G(R), A-T(R), A-G(N)6 19 0.65 T-T(R), T-C(R), A-T(R), A-C(N) 6 25 0.65 T-T(R), T-G(R),A-T(R), A-G(N) 6 26 0.65 T-T(R), T-C(R), A-T(R), A-C(N) 6 27 0.65T-A(R), T-C(R), A-A(R), A-C(N) 7 18 0.65 A-T(R), A-G(R), G-T(R), G-G(N)7 19 0.65 A-T(R), A-C(R), G-T(R), G-C(N) 7 25 0.65 A-T(R), A-G(R),G-T(R), G-G(N) 7 26 0.65 A-T(R), A-C(R), G-T(R), G-C(N) 7 27 0.65A-A(R), A-C(R), G-A(R), G-C(N) 1 29 0.63 T-A(R), T-T(R), TA-A(R),TA-T(N) 2 29 0.63 T-A(R), T-T(R), TAT-A(R), TAT-T(N) 3 29 0.63 G-A(R),G-T(R), A-A(R), A-T(N) 10 36 0.62 T-T(R), T-C(R), A-T(R), A-C(N) 11 340.62 T-C(R), T-A(R), C-C(R), C-A(N) 12 34 0.62 T-C(R), T-A(R), G-C(R),G-A(N) 14 20 0.62 G-G(R), G-A(R), A-G(R), A-A(N) 1 8 0.61 T-A(R),T-G(R), TA-A(R), TA-G(N) 2 8 0.61 T-A(R), T-G(R), TAT-A(R), TAT-G(N) 3 80.61 G-A(R), G-G(R), A-A(R), A-G(N) 4 29 0.61 A-A(R), A-T(R), G-A(R),G-T(N) 5 29 0.61 A-A(R), A-T(R), T-A(R), T-T(N) 6 29 0.61 T-A(R),T-T(R), A-A(R), A-T(N) 7 29 0.61 A-A(R), A-T(R), G-A(R), G-T(N) 21 320.6 T-G(R), T-C(R), G-G(R), G-C(N) 21 33 0.6 T-C(R), T-T(R), G-C(R),G-T(N) 21 35 0.6 T-A(R), T-G(R), G-A(R), G-G(N)

TABLE 4Sequences of the DNA polymorphisms listed in Table 1 and Table 2. Thenumbering is the same as the numbering in Table 1 and Table 2. DNApolymor- phism # DNA polymorphism name DNA sequence 1AGKD01281000.1_41.57[T/TA] AAGTTCTTTTTTTTT[−/A]TATATGACTATCCTT[Seq . ID No.: 1/Seq. ID No.: 37] 2 AGKD01281000.1_5527[T/TAT]TTGAGCACGTGTTTT[−/AT]GACGGTGTAGGAAGT [Seq . ID No.: 2/Seq. ID No.: 38] 3AGKD01021775.1_19790[G/A] ACGTACGCAGGCGCA[C/T]CCCTGCGATTTAGTG[Seq . ID No.: 3/Seq. ID No.: 39] 4 AGKD01281000.1_5251[A/G]GGGAGGTCAGTGGGG[C/T]AGACAACTTAAAGCA [Seq . ID No.: 40/Seq. ID No.: 4] 5AGKD01281000.1_4338[A/T] TCTTCAGGAAAAAAA[A/T]ATATAATTAGTGATT[Seq . ID No.: 5/Seq. ID No.: 41] 6 AGKD01317469.1_245[T/A]CTACAAACTTTCTCA[A/T]GGTATAGCAAAAAAT [Seq . ID No.: 42/Seq. ID No.: 6] 7AGKD01281000.1_5457[A/G] GAATGAAAGCACTTT[C/T[TTGGTATCCTATGCT[Seq . ID No.: 43/Seq. ID No.: 7] 8 AGKD01028155.1_12812[A/G]GTCCTAACATTGAGC[C/T]GTGTTTGTTTGGCAG [Seq . ID No.: 44/Seq. ID No.: 8] 9AGKD01452978.1_5956[A/G] ACTATTTTATCTGGC[C/T]CTTTCAATCAGTCCT[Seq . ID No.: 45/Seq. ID No.: 9] 10 AGKD01039267.1_12921[T/A]GATGATGGCCCCTAG[A/T]GAGTTACTGTAATGA [Seq . ID No.: 10/Seq. ID No.: 46]11 AGKD01059002.1_4664[T/C] ACATTATAAAAACAG[C/T]ATGAAGTGTACGTGT[Seq . ID No.: 47/Seq. ID No.: 11] 12 AGKD01451885.1_830[T/G]CAGACAGACACCTAC[A/C]AGTAGGCTATGTGTT [Seq . ID No.: 12/Seq. ID No.: 48]13 AGKD01003456.135321[A/G] ACAAAGTAAGGTGGG[C/T]GGTGCAGAGTTAGGC[Seq . ID No.: 49/Seq. ID No.: 13] 14 AGKD01059002.1_16264[G/A]AGTTTCAAATGAAAT[A/G]TGAATCCTTCAGGAT [Seq . ID No.: 50/Seq. ID No.: 14]15 AGKD01452978.1_6935[A/G] GGTGAAATCATCGTG[C/T]ATAGGCTATCACAGT[Seq . ID No.: 51/Seq. ID No.: 15] 16 AGKD01003456.1_36664[G/T]GAGTACAGTGCACTC[A/C]GACAGACAGGCACAC [Seq . ID No.: 52/Seq. ID No.: 16]17 AGKD01340746.1_282[C/T] TTTTTGAGGAGGAGG[A/G]AAATACATTGTGTTC[Seq . ID No.: 53/Seq. ID No.: 17] 18 AGKD01062103.1_13615[T/G]TCTTTCACACATGAC[G/T]CCGTAATCCCGTTAC [Seq . ID No.: 54/Seq. ID No.: 18]19 AGKD01062103.1_13695[T/C] GCAGGCAGCGCTTGA[C/T]GGCGAATTGTTTTGA[Seq . ID No.: 55/Seq. ID No.: 19] 20 AGKD01007787.1_13666[G/A]CATTTTATGCATTAT[A/G]TATCAGTGATGTTAC [Seq . ID No.: 56/Seq. ID No.: 20]21 AGKD01059002.1_3603[T/G] AGACATAGGCTCAAA[G/T]AATTCCTCACTGAGG[Seq . ID No.: 57/Seq. ID No.: 21] 22 AGKD01000927.1_15806[C/G]AGTGTGTTGCACATC[C/G]TGTCATGCAGACAAT [Seq . ID No.: 22/Seq. ID No.: 58]23 AGKD01458345.1_5634[G/T] CACACTTTGTCAACA[A/C]ACACATATTATGTTA[Seq . ID No.: 23/Seq. ID No.: 59] 24 AGKD01083029.1_8368[A/C]CTGCTAATGTCCTTT[G/T]GTGGGTTTCTTTTGG [Seq . ID No.: 24/Seq. ID No.: 60]25 AGKD01062103.1_13615[T/G] GTAACGGGATTACGG[A/C]GTCATGTGTGAAAGA[Seq . ID No.: 25/Seq. ID No.: 61] 26 AGKD01062103.1_13695[T/C]TCAAAACAATTCGCC[A/G]TCAAGCGCTGCCTGC [Seq . ID No.: 26/Seq. ID No.: 62]27 AGKD01032349.1_7232[A/C] ACTCCCAGTGCTAAG[G/T]GAAGTCTCCAACATT[Seq . ID No.: 63/Seq. ID No.: 27] 28 AGKD01032349.1_14078[A/G]CCTCCTCTCCCTCCC[A/G]GAGTCTGATGCAATT [Seq . ID No.: 64/Seq. ID No.: 28]29 AGKDO1051656.1_1495[T/A] ATTCATTAATCCAGC[A/T]ATAGTTACTGGCACC[Seq . ID No.: 29/Seq. ID No.: 65] 30 AGKD01083029.1_5084[G/C]TGCCAGAGACCCCCA[C/G]TGGAGCGTTCAGGGT [Seq . ID No.: 66/Seq. ID No.: 30]31 AGKD01455926.1_1814[G/A] AGTCAACCGCAGTAC[C/T]GAAGCAAGACTGTAG[Seq . ID No.: 67/Seq. ID No.: 31] 32 AGKD01003456.1_1873[G/C]CGGACCAGGAGACAG[C/G]GACCCATCATTTCAT [Seq . ID No.: 32/Seq. ID No.: 68]33 AGKD01037589.1_572[C/T] GCAATGTTCATCCTG[C/T]TTAATTCACCAAATG[Seq . ID No.: 33/Seq. ID No.: 69] 34 AGKD01037589.1_1369[C/A]CGCTACAGAAATGAC[A/C]GAAAATACACACTTC [Seq . ID No.: 70/Seq. ID No.: 34]35 AGKD01205804.1_11559[A/G] AGATTTAGGAGGGTT[C/T]GCTCAAAATAAGAAA[Seq . ID No.: 71/Seq. ID No.: 35] 36 AGKD01106761.1_1717[T/C]TTATTCGGTGGTACC[C/T]ACTCTCAGAAATCTT [Seq . ID No.: 72/Seq. ID No.: 36]

5. PROVING THE EFFECT OF THE SNP-ASSISTED SELECTION

Challenge 1: An experiment was set up in order to test the effect ofimplementing the SNP-haplotype-based DNA-test described above (1-4):Using the haplotype-based DNA test (with marker pair 3+23, see Table 3),4 non-resistant males, 6 resistant males, 6 non-resistant females, and 4resistant females were selected from the Aqua Gen breeding population.All males were crossed to all females, producing the four groups R×R,R×N, N×R, and N×N; R×R consisting of the offspring of resistant malesand resistant females, R×N consisting of the offspring of resistantmales and non-resistant females, N×R consisting of the offspring ofnon-resistant males and resistant females, and N×N consisting of theoffspring of non-resistant males and non-resistant females. The groupswere transported to the challenge test facilities (VESO Vikan, Namsos,Norway) at the average size of 0.2 g (pre-startfed fry), start-fedwithin 1 day of arrival, acclimatized according to Standard OperationProcedure (SOP)S-2023, tended and monitored on a daily basis accordingto S-2002 and S-2004. Dead fish were collected every day according toS-2000, and the mortalities were recorded. Environmental parameters wererecorded daily. Each of the groups R×R, R×N, N×R, and N×N were tested intwo tanks following a bath challenge model (S-1079). Each tank had 100fry from the corresponding group. Fresh (i.e. not frozen) infectiouspancreatic necrosis virus was used, coming from an isolate of serotypeSP 1, passage j.no. V-1244 (Norwegian field isolate from 2001), growthand titration of the virus being done at the Norwegian School ofVeterinary Science (Oslo, Norway). Two additional tanks were included ascontrols, containing mock-challenged fish from all four groups. Resultsare provided in FIG. 1.

FIG. 1: Mortalities in an IPN challenge test performed on four differentgroups of fish produced using the method described in this application.According to the test, all fish in group N×N were non-resistant, allfish in groups N×R and R×N were semi-resistant, while all fish in groupR×R were resistant. The fish in the control group were mock challenged,so that the mortalities in this group represent expected mortalities inthe absence of virus.

Challenge 2: This experiment was set up in order to compare themortality due to the standard virus isolate V-1244 isolated in 2001 withthe mortality due to a Norwegian field strain isolated in 2012 from ahatchery experiencing IPN-related mortality. Using the haplotype-basedDNA test (with marker pair 3+23, see Table 3), 6 non-resistant males, 5resistant males, 6 non-resistant females, and 6 resistant females wereselected from the Aqua Gen breeding population. All males were crossedto all females, producing the four groups R×R, R×N, and N×N; R×Rconsisting of the offspring of resistant males and resistant females,R×N consisting of the offspring of resistant males and non resistantfemales as well as the offspring of non-resistant males and resistantfemales, and N×N consisting of the offspring of non-resistant males andnon-resistant females. The groups were transported to the challenge testfacilities (VESO Vikan, Namsos, Norway) at the average size of 0.2 g(pre-startfed fry), startfed within 1 day of arrival, acclimatizedaccording to Standard Operation Procedure (SOP)S-2023, tended andmonitored on a daily basis according to S-2002 and S-2004. Dead fishwere collected every day according to S-2000, and the mortalities wererecorded. Environmental parameters were recorded daily. Each of thegroups R×R. R×N and N×N were tested in two parallel tanks for each virusstrain (V-1244 and field strain) following a bath challenge model(S-1079). Each tank had 100 fry from the corresponding group. The V-1244strain isolated in 2001 was prepared by the Norwegian School ofVeterinary Science (Oslo, Norway), whereas the field strain waspropagated and titrated by Vaxxinova Norway. Both virus isolates werekept refrigerated until challenge. One tank was included as a negativecontrol, containing mock-challenged fish of all three genotypes. Thechallenge was terminated 45 days after challenge, and the results areprovided. In FIGS. 2 and 3. The results demonstrate that the R×R fish(as determined by the methods of the present invention) are fullyresistant to both IPNV strains.

FIG. 2.

Cumulative mortality in a bath challenge of Atlantic salmon fry ofdiffering IPN QTL genotypes challenged with a well known test isolate ofIPNV, V-1244 (FIG. 2) or with a field strain Isolated from a hatchery in2012 (FIG. 3).

6. COMPARISON OF KNOWN SNPS WITH THOSE OF THE PRESENT INVENTION

Houston et al. (2012 Identified single nucleotide polymorphisms (SNPs)that were alleged to be associated with resistance to IPN. In theirpaper they reveal two SNPs (called Ssa0139ECIG and RAD_HT01) that arereported to have a particularly strong association to IPN-resistance.The SNP Ssa0139ECIG was first reported in a paper by Moen et al., butthat study did not report any association to IPN-resistance. RAD_HT01was reported for the first time by Houston et al. (2012).

The SNP RAD_HT01 was independently identified by the applicant as partof the sequencing-based screening for DNA polymorphisms associated withIPN resistance discussed above. However, the estimated association toIPN-resistance was found to be too weak to warrant further testing bygenotyping; the p-value (the significance level of the SNP was 0.0199,whereas all the SNPs selected for testing by the present applicantgenotyping had p-values below 0.005).

The SNP Ssa0139ECIG was not independently identified by the applicant,as this SNP was not covered by the reference DNA sequence used in theapplicant's search for IPN-associated SNPs. Instead, the associationbetween this SNP and IPN-resistance was tested by the applicant bygenotyping the parents of IPN challenged fish, followed by statisticaltesting of the effect of SNP genotypes in these parents on mortalityrates in their offspring (in the same manner discussed above for thepresent invention). The data set consisted of 285 full-sib groups withrecorded mortality rates and genotyped parents. The SNPAGKD01021775.1_19790[G/A] provided in Table 1 was included in theanalysis, as a positive control. The association between the SNP and IPNresistance was tested using this linear model (one SNP at a time):

y=1μ+(Z _(s) +Z _(d))u+pb+e

where y is a vector of mortality rates for all full-sib groups, μ is theoverall mean, u is a vector of random additive genetic effects ofparents, Z_(s) and Z_(d) are sire and dam incidence matrices, p is avector of SNP allele copies in the parents (0-4) for each full-sibgroup, b is the random regression coefficient associated with number ofparental SNP alleles, and e is a vector of random residuals.Furthermore, u˜N(0, Aσ_(u) ²), b˜N(0,σ_(b) ²), and e˜N(0,Iσ_(e) ²),where A is the numerator relationship matrix for the parents, o_(u)²=¼σ_(g) ², σ_(g) ² is the total additive genetic (polygenic) variance,σ_(b) ² is the variance of the random regression coefficient and σ_(e) ²is the residual variance of full-sib group mortality rates.

Variance components were estimated for all random effects (additivegenetic sire & dam, random regression of SNP effect and residual), usingREML methodology with the DMU software (Madsen and Jensen 2008. To testthe significance of a SNP, the full model was compared to a reducedmodel without the random regression on number of parental SNP alleles,using a likelihood ratio test.

The SNP Ssa0139ECIG was found to have no significant effect on the IPNmortality (p-value=0.64), whereas the SNP AGKD01021775.1_19790[G/A] wereextremely significant (p-value=2.86E-18).

In the paper by Houston et al. (2012), the SNPs Ssa0139ECIG and RAD_HT01are presented as having strong (and approximately equal) effects onIPN-resistance. The results described above indicate that the DNApolymorphisms described by Houston et al. (2012) have little or noeffect on IPN resistance in the population tested, while the DNApolymorphisms described in the present application have strong andextremely significant effects.

REFERENCES

-   Houston R D, Haley C S, Hamilton A, Guy D R, Tinch A E, Taggart J B,    McAndrew B J, Bishop S C (2008) Major quantitative trait loci affect    resistance to infectious pancreatic necrosis in Atlantic salmon    (Salmo salar). Genetics 178: 1109-15.-   Houston R D, Davey J W, Bishop S C, Lowe, N R, Mota-Velasco J C et    al. (2012) Characterisation of QTL-linked and genome-wide    restriction site-associated DNA (RAD) markers in farmed Atlantic    salmon. BMC Genomics 13: 244,-   Lien S, Gidskehaug L, Moen T, Hayes B J, Berg P R, Davidson W S,    Omholt S W, Kent M P (2011) A dense SNP-based linkage map for    Atlantic salmon (Salmo salar) reveals extended chromosome    homeologies and striking differences in sex-specific recombination    patterns. BMC Genomics 12: 615.-   Madsen and Jensen (2008) DMU: a user's guide. A package for    analysing multivariate mixed models, version 6, release 5.0.    University of Aarhus, Tjele, Denmark.-   Moen T, Hayes B, Baranski M, Berg P R, Kjøglum S, Koop B F, Davidson    W S, Omholt S W, Lien S (2008) A linkage map of the Atlantic salmon    (Salmo solar) based on EST-derived SNP markers. BMC Genomics 9: 223.-   Moen T, Baranski M, Sonesson A K, Kjøglum S (2009) Confirmation and    fine-mapping of a major QTL for resistance to infectious pancreatic    necrosis in Atlantic salmon (Salmo solar): population-level    associations between markers and trait. BMC Genomics 10: 368.-   Shifman S, Kuypers J, Kokoris M, Yakir B, Darvasi A (2003) Linkage    diseuilibrium patterns of the human genome across populations. Human    Molecular Genetics 12: 771-776.-   Thorsen J, Zhu B, Frengen E, Osoegawa K, de Jong, P J, Koop B F,    Davidson W S, Hoyheim B (2005) A highly redundant BAC library of    Atlantic salmon (Salmo salar): an Important tool for salmon    projects. BMC Genomics 6: 50.

1-10. (canceled)
 11. A method for detecting a DNA polymorphism inAtlantic salmon, the method comprising: obtaining a tissue sample froman Atlantic salmon; extracting DNA from said tissue sample; providing anallele-specific polynucleotide that hybridizes to either allele #1 orallele #2 of the DNA polymorphism, wherein the DNA polymorphism andallele #1 and allele #2 are: DNA Polymorphism No. DNA polymorphism nameallele #1/allele #2 1 AGKD01281000.1_4157 T/TA

hybridizing the allele-specific polynucleotide to the extracted DNA; anddetecting allele #1 or allele #2 of the DNA polymorphism based on thehybridization of the allele-specific polynucleotide to the extractedDNA.
 12. The method of claim 11 wherein a fragment of DNA is used assaid extracted DNA.
 13. The method of claim 12 wherein said hybridizingstep is employed in a process selected from polymerase chain reaction,probe hybridization, and competitive hybridization of probes.
 14. Amethod of obtaining Atlantic salmon to be used as broodstock, the methodcomprising the steps of: carrying out the method as claimed in claim 11;and physically separating from a population of Atlantic salmon thoseAtlantic salmon in which allele #1 of the DNA polymorphism is detected,thereby obtaining Atlantic salmon to be used as broodstock.
 15. Themethod of claim 11 wherein the method further comprises providing atleast one additional allele-specific polynucleotide that hybridizes toeither allele #1 or allele #2 of one of the following DNA polymorphisms;hybridizing the at least one additional allele-specific polynucleotideto the extracted DNA; and detecting hybridization of the at least oneadditional allele-specific polynucleotide to the extracted DNA asindicative of the presence of allele #1 or allele #2 of one of thefollowing DNA polymorphisms: DNA Polymorphism No. DNA polymorphism nameallele #1/allele #2 2 AGKD01281000.1_5527 T/TAT 3 AGKD01021775.1_19790G/A 4 AGKD01281000.1_5251 A/G 5 AGKD01281000.1_4338 A/T


16. The method of claim 11, wherein the method further comprisesproviding at least one additional allele-specific polynucleotide thathybridizes to either allele #1 or allele #2 of one of the following DNApolymorphisms; hybridizing the at least one additional allele-specificpolynucleotide to the extracted DNA; and detecting hybridization of theat least one additional allele-specific polynucleotide to the extractedDNA as indicative of the presence of allele #1 or allele #2 of one ofthe following DNA polymorphisms: DNA polymorphism No. DNA polymorphismName allele #1/allele #2 2 AGKD01281000.1_5527 T/TAT 3AGKD01021775.1_19790 G/A 4 AGKD01281000.1_5251 A/G 5 AGKD01281000.1_4338A/T 6 AGKD01317469.1_245 T/A 7 AGKD01281000.1_5457 A/G 8AGKD01028155.1_12812 A/G 9 AGKD01452978.1_5956 A/G 10AGKD01039267.1_12921 T/A 11 AGKD01059002.1_4664 T/C 12AGKD01451885.1_830 T/G 13 AGKD01003456.1_35321 A/G 14AGKD01059002.1_16264 G/A 15 AGKD01452978.1_6935 A/G 16AGKD01003456.1_36664 G/T 17 AGKD01340746.1_282 C/T 18AGKD01062103.1_13615 T/G 19 AGKD01062103.1_13695 T/C 20AGKD01007787.1_13666 G/A 21 AGKD01059002.1_3603 T/G 22AGKD01000927.1_15806 C/G 23 AGKD01458345.1_5634 T/G 24AGKD01083029.1_8368 C/A 25 AGKD01062103.1_13615 T/G 26AGKD01062103.1_13695 T/C 27 AGKD01032349.1_7232 A/C 28AGKD01032349.1_14078 G/A 29 AGKD01051656.1_1495 A/T 30AGKD01083029.1_5084 C/G 31 AGKD01455926.1_1814 A/G 32AGKD01003456.1_1873 G/C 33 AGKD01037589.1_572 C/T 34 AGKD01037589.1_1369C/A 35 AGKD01205804.1_11559 A/G 36 AGKD01106761.1_1717 T/C